What is machine learning? Everything you need to know
Machine learning is enabling computers to tackle tasks that have, until now, only been carried out by people.
From driving cars to translating speech, machine learning is driving an explosion in the capabilities of artificial intelligence — helping software make sense of the messy and unpredictable real world.
But what exactly is machine learning and what is making the current boom in machine learning possible?
At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data.
Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word bookin a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video.
The key difference from traditional computer software is that a human developer hasn’t written code that instructs the system how to tell the difference between the banana and the apple.
Instead a machine-learning model has been taught how to reliably discriminate between the fruits by being trained on a large amount of data, in this instance likely a huge number of images labelled as containing a banana or an apple.
Data, and lots of it, is the key to making machine learning possible.
Machine learning may have enjoyed enormous success of late, but it is just one method for achieving artificial intelligence.
At the birth of the field of AI in the 1950s, AI was defined as any machine capable of performing a task that would typically require human intelligence.
AI systems will generally demonstrate at least some of the following traits: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity.
Alongside machine learning, there are various other approaches used to build AI systems, including evolutionary computation, where algorithms undergo random mutations and combinations between generations in an attempt to “evolve” optimal solutions, and expert systems, where computers are programmed with rules that allow them to mimic the behavior of a human expert in a specific domain, for example an autopilot system flying a plane.
Machine learning is generally split into two main categories: supervised and unsupervised learning.
This approach basically teaches machines by example.
During training for supervised learning, systems are exposed to large amounts of labelled data, for example images of handwritten figures annotated to indicate which number they correspond to. Given sufficient examples, a supervised-learning system would learn to recognize the clusters of pixels and shapes associated with each number and eventually be able to recognize handwritten numbers, able to reliably distinguish between the numbers 9 and 4 or 6 and 8.
However, training these systems typically requires huge amounts of labelled data, with some systems needing to be exposed to millions of examples to master a task.
Training datasets for these systems can run to millions of examples, with Google’s Open Images Dataset having about nine million images, its labeled video repositoryYouTube-8M linking to seven million labeled videos and ImageNet, one of the early databases of this kind, having more than 14 million categorized images. The size of training datasets continues to grow, with Facebook recently announcing it had compiled 3.5 billion images publicly available on Instagram, using hashtags attached to each image as labels. Using one billion of these photos to train an image-recognition system yielded record levels of accuracy — of 85.4 percent — on ImageNet’s benchmark.
